Cardiac MRI Segmentation Method and System Based on Adversarial Network

Abstract

The present disclosure discloses a cardiac MRI segmentation method and system based on an adversarial network, and the adversarial network includes a segmenter and a discriminator. The specific steps include: inputting the cardiac MRI to be segmented; preprocessing the input cardiac MRI to be segmented; training an adversarial network; invoking the segmenter model trained by the adversarial to segment the preprocessed cardiac MRI; completing the ventricular structure segmentation, Output the segmentation results of left ventricle, right ventricle and myocardium. The accuracy of the segmented left ventricle, right ventricle and myocardium was evaluated by Dice coefficient and Horsdorff distance. The evaluation results show that this method can automatically output high-precision cardiac MRI segmentation results, thereby alleviating the shortage of public health resources and improving the efficiency of doctors' diagnosis.

Description

technical field [0001] The present disclosure relates to the technical field of medical image processing, in particular to a cardiac MRI segmentation method and system based on an adversarial network. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] With the development of medical technology and the improvement of public health conditions, the fatality rate of epidemics has continued to decline. Correspondingly, chronic diseases have become the main cause of death for modern people. The heart is the power pump for blood circulation, and lesions in the heart area are extremely fatal. With the improvement of modern people's living standards, cardiovascular disease has become the number one cause of death for humans. Before medical image technology was applied to clinical diagnosis, doctors could only diagnose heart disease through electrocardiogra...

AI Technical Summary

Problems solved by technology

However, manual segmentation is time-consuming and labor-intensive. Since the shape of the heart is different and the pathological features vary widely, the segmentation results of different doctors are inconsistent, and the same doctor may also have different results in two segmentations, which leads to poor repeatability of manual segmentation.

[0008] To sum up, most of the current ventricular segmentation algorithms have shortcomings such as excessive calculation and complicated steps.

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